ed6171893a9d4e75df11a27f26a19f79d349ea2d,benchmarks/sru.py,SRU,__init__,#SRU#Any#Any#Any#Any#Any#Any#Any#Any#,508

Before Change

        self.depth = num_layers
        self.dropout = dropout
        self.rnn_dropout = rnn_dropout
        self.rnn_lst = nn.ModuleList()
        self.bidirectional = bidirectional
        self.out_size = hidden_size*2 if bidirectional else hidden_size

        for i in range(num_layers):
            l = SRUCell(
                n_in = self.n_in if i==0 else self.out_size,
                n_out = self.n_out,
                dropout = dropout if i+1 != num_layers else 0,
                rnn_dropout = rnn_dropout,
                bidirectional = bidirectional,
                use_tanh = use_tanh,
                use_relu = use_relu,
            )
            self.rnn_lst.append(l)

    def set_bias(self, bias_val=0):
        for l in self.rnn_lst:
            l.set_bias(bias_val)

After Change

        self.dropout = dropout
        self.bidirectional = bidirectional
        self.activation_type = 2 if use_relu else (1 if use_tanh else 0)
        self.use_kernel = use_kernel
        out_size = n_out*2 if bidirectional else n_out
        k = 4 if n_in != out_size else 3
        self.size_per_dir = n_out*k
        self.weight = nn.Parameter(torch.Tensor(
            n_in,
            self.size_per_dir*2 if bidirectional else self.size_per_dir
        ))
        self.bias = nn.Parameter(torch.Tensor(
            n_out*4 if bidirectional else n_out*2
        ))
        self.init_weight()

    def init_weight(self):
        val_range = (3.0/self.n_in)**0.5

Instances